More details for the experiments in Section .
(a) Compared with the original COCO 2017 dataset, our down-sampled COCO 2017 dataset have a much smaller number of training images. We should note that the image resolution of the sampled images is kept unchanged. Thus a more precise description for the sampled dataset may be sub-sampled COCO 2017 dataset. All experiments on the down-sampled COCO 2017 are trained for 50 epochs.
(b) For the ablation study on the number of multi-scale features, the 64\(\times \)  down-sampled feature is obtained by applying a 3\(\times \) 3 convolution layer with a stride of 2 on the 32\(\times \)  down-sampled feature, following DeformDETR [1]}.
(c) For the evaluation of generalization to the sample-rich dataset, we re-implement DETR and CondDETR on COCO 2017 dataset for a fair comparison with our method.
